If it were not for the great variability among individuals, medicine might as well be a science, not an art.

—Sir William Osler, 1892

The proliferation of data collected in routine clinical care and advances in quantitative analytic methods have created immediate opportunities to individualize cardiovascular care through the use of decision tools. Such tools rely on the analysis of simple variables collected from medical records or direct patient observation to identify those with the most to gain or lose from treatment, as determined by the collective experiences of similar patients treated previously. However, a number of obstacles limit the creation and use of decision tools. Here, we review the theoretical foundation of decision tools, highlight examples of successful efforts to model heterogeneity in treatment benefit, and suggest future goals to better use quantitative methods to personalize care.

Identifying Heterogeneous Treatment Reponses

Arguments in favor of decision tools rest on 3 premises. First, differences in risk between patients must be identifiable by the tool more reliably than by clinical judgment alone (identifiable heterogeneity). Second, the identified risks should be modifiable by clinical decisions (actionability). Third, the tool should be able to be adopted into practice without disruption to patient care or work flow (implementability).

Although the requirement for patient heterogeneity to enable personalized medicine may be self-evident, much of the literature that currently shapes cardiovascular practice fails to offer meaningful information to help clinicians identify or act on heterogeneity. Randomized trials typically report the overall treatment effect observed in the entire study sample as …